Publications
heFFTe: Highly Efficient FFT for Exascale (Poster)
, Seattle, WA, SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP20), February 2020.
(1.54 MB)
(1.54 MB)
heFFTe: Highly Efficient FFT for Exascale (Poster)
: NVIDIA GPU Technology Conference (GTC2020), October 2020.
(866.88 KB)
(866.88 KB)
“heFFTe: Highly Efficient FFT for Exascale,”
International Conference on Computational Science (ICCS 2020), Amsterdam, Netherlands, June 2020.
DOI: 10.1007/978-3-030-50371-0_19 (2.62 MB)
(2.62 MB)
“Harnessing the Computing Continuum for Programming Our World,”
Fog Computing: Theory and Practice: John Wiley & Sons, Inc., 2020.
DOI: 10.1002/9781119551713.ch7 (1.4 MB)
(1.4 MB)
Harnessing GPU's Tensor Cores Fast FP16 Arithmetic to Speedup Mixed-Precision Iterative Refinement Solvers and Achieve 74 Gflops/Watt on Nvidia V100
, San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.
(2.96 MB)
(2.96 MB)
“Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers,”
The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Dallas, TX, IEEE, November 2018.
DOI: 10.1109/SC.2018.00050 (642.51 KB)
(642.51 KB)
“Hardware-Counter Based Automatic Performance Analysis of Parallel Programs,”
Advances in Parallel Computing, vol. 13, Dresden, Germany, Elsevier, pp. 753-760, January 2004, 2003.
DOI: 10.1016/S0927-5452(04)80092-3
“Hands-on Research and Training in High-Performance Data Sciences, Data Analytics, and Machine Learning for Emerging Environments,”
ISC High Performance, Frankfurt, Germany, Springer International Publishing, June 2019.
(1016.52 KB)
(1016.52 KB)
“HAN: A Hierarchical AutotuNed Collective Communication Framework,”
IEEE Cluster Conference, Kobe, Japan, Best Paper Award, IEEE Computer Society Press, September 2020.
(764.05 KB)
(764.05 KB)
“A Guide for Achieving High Performance with Very Small Matrices on GPUs: A Case Study of Batched LU and Cholesky Factorizations,”
IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 5, pp. 973–984, May 2018.
DOI: 10.1109/TPDS.2017.2783929 (832.92 KB)
(832.92 KB)
“GPUDirect MPI Communications and Optimizations to Accelerate FFTs on Exascale Systems,”
EuroMPI'19 Posters, Zurich, Switzerland, no. icl-ut-19-06: ICL, September 2019.
(2.25 MB)
(2.25 MB)
“GPU-based LU Factorization and Solve on Batches of Matrices with Band Structure,”
SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023.
DOI: 10.1145/3624062.3624247
“GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement,”
EuroPar 2012 (also LAWN 260), Rhodes Island, Greece, August 2012.
(662.98 KB)
(662.98 KB)
“GPU algorithms for Efficient Exascale Discretizations,”
Parallel Computing, vol. 108, pp. 102841, 2021.
DOI: 10.1016/j.parco.2021.102841
“Give MPI Threading a Fair Chance: A Study of Multithreaded MPI Designs,”
IEEE Cluster, Albuquerque, NM, IEEE, September 2019.
(220.84 KB)
(220.84 KB)
“Ginkgo—A math library designed for platform portability,”
Parallel Computing, vol. 111, pp. 102902, February 2022.
DOI: 10.1016/j.parco.2022.102902
Ginkgo: A Node-Level Sparse Linear Algebra Library for HPC (Poster)
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(699 KB)
(699 KB)
“Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing,”
ACM Transactions on Mathematical Software, vol. 48, issue 12, pp. 1 - 33, March 2022.
DOI: 10.1145/3480935 (4.2 MB)
(4.2 MB)
“Ginkgo - A math library designed to accelerate Exascale Computing Project science applications,”
The International Journal of High Performance Computing Applications, August 2024.
DOI: 10.1177/10943420241268323
“Ginkgo: A High Performance Numerical Linear Algebra Library,”
Journal of Open Source Software, vol. 5, issue 52, August 2020.
DOI: 10.21105/joss.02260 (721.84 KB)
(721.84 KB)
Gingko: A Sparse Linear Algebrea Library for HPC
: 2021 ECP Annual Meeting, April 2021.
(893.04 KB)
(893.04 KB)
“Generic Matrix Multiplication for Multi-GPU Accelerated Distributed-Memory Platforms over PaRSEC,”
ScalA'19: 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Denver, CO, IEEE, November 2019.
(260.69 KB)
(260.69 KB)
“A Generic Approach to Scheduling and Checkpointing Workflows,”
The 47th International Conference on Parallel Processing (ICPP 2018), Eugene, OR, IEEE Computer Society Press, August 2018.
(737.11 KB)
(737.11 KB)
“A Generic Approach to Scheduling and Checkpointing Workflows,”
International Journal of High Performance Computing Applications, vol. 33, issue 6, pp. 1255-1274, November 2019.
DOI: 10.1177/1094342019866891 (555.01 KB)
(555.01 KB)
“A Generic Approach to Scheduling and Checkpointing Workflows,”
Int. Journal of High Performance Computing Applications, vol. 33, no. 6, pp. 1255-1274, 2019.
(555.01 KB)
(555.01 KB)
Generalizing Random Butterfly Transforms to Arbitrary Matrix Sizes
: arXiv, December 2023.
“Generalized Flow-Graph Programming Using Template Task-Graphs: Initial Implementation and Assessment,”
2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Lyon, France, IEEE, July 2022.
DOI: 10.1109/IPDPS53621.2022.00086
The Future of Computing: Software Libraries
, Savannah, GA, DOD CREATE Developers' Review, Keynote Presentation, February 2012.
(6.76 MB)
(6.76 MB)
“From Serial Loops to Parallel Execution on Distributed Systems,”
International European Conference on Parallel and Distributed Computing (Euro-Par '12), Rhodes, Greece, August 2012.
(203.08 KB)
(203.08 KB)
“From MPI to OpenSHMEM: Porting LAMMPS,”
OpenSHMEM and Related Technologies. Experiences, Implementations, and Technologies, Annapolis, MD, USA, Springer International Publishing, pp. 121–137, 2015.
DOI: 10.1007/978-3-319-26428-8_8
